Identification and Classification of Talk-Over Segments During Voice Communications Using Machine Learning Models

4. The voice analysis system of claim 1, wherein the first audio signal comprises a stereo voice conversation input between the at least two users from at least two devices.

5. The voice analysis system of claim 1, wherein the ML model is trained using one of a linear regression algorithm, a random forest algorithm, a gradient boosting algorithm, or a binary classification ML algorithm, and wherein the ML model is configured to be executed in a real-time production computing environment associated with the incoming voice call or in an offline computing environment that provides analytics for agent review for an agent for the at least two users.

8. The voice analysis system of claim 1, wherein the speech segment parameters are associated with at least one of a voice activity detector (VAD) or a speech activity detector (SAD) configured to detect a presence or an absence of speech in the first speech.

12. The method of claim 9, wherein the first audio signal comprises a stereo voice conversation input between the at least two users from at least two devices.

13. The method of claim 9, wherein the ML model is trained using one of a linear regression algorithm, a random forest algorithm, a gradient boosting algorithm, or a binary classification ML algorithm, and wherein the ML model is configured to be executed in a real-time production computing environment associated with the incoming voice call or in an offline computing environment that provides analytics for agent review for an agent for the at least two users.

16. The method of claim 9, wherein the speech segment parameters are associated with at least one of a voice activity detector (VAD) or a speech activity detector (SAD) configured to detect a presence or an absence of speech in the first speech.

20. The non-transitory computer-readable medium of claim 17, wherein the first audio signal comprises a stereo voice conversation input between the at least two users from at least two devices.